| Yazarlar (4) |
Ergin Kalkan
Türkiye |
Öğr. Gör. Dr. İbrahim BUDAK
Kastamonu Üniversitesi, Türkiye |
|
Gürkan Kaya
Türkiye |
|
Elif Gül Aydın
Türkiye |
| Özet |
| Effective medical waste planning relies on the reliable estimation of waste volumes. As operational factors diversify, traditional linear regressions often fail to capture the underlying structure, whereas latent variable–based and ensemble approaches can better represent this complexity. In this study, fine-tuned Partial Least Squares (PLS), scikit-learn–based Gradient Boosting regression (GBR), and a baseline Ordinary Least Squares (OLS) model were compared for estimating medical waste generation using 48 months (2021–2024) of approximate data from Dental Clinics affiliated with the Provincial Health Directorate in Kastamonu. The model inputs were the monthly procedure counts for endodontics, treatment, prosthetics, periodontology, orthodontics, pedodontics, and surgery. Performance was evaluated using Root Mean Square Error (RMSE), Mean Absolute Error (MAE) and R-squared (R2). All models produced accurate predictions; however, PLS provided the strongest fit (R2 = 0.979; MAE = 30.488; RMSE = 37.043), outperforming GBR (R2 = 0.962; MAE = 36.544; RMSE = 48.990) and the OLS baseline (R2 = 0.927; MAE = 41.762; RMSE = 59.013). The findings demonstrate that modern, data-driven waste-management planning is feasible in healthcare institutions and highlight PLS as a robust option, particularly under conditions of small sample size and collinearity. |
| Anahtar Kelimeler |
| Makale Türü | Özgün Makale |
| Makale Alt Türü | SSCI, AHCI, SCI, SCI-Exp dergilerinde yayınlanan tam makale |
| Dergi Adı | Processes |
| Dergi ISSN | 2227-9717 Wos Dergi Scopus Dergi |
| Dergi Tarandığı Indeksler | SCI |
| Dergi Grubu | Q3 |
| Makale Dili | İngilizce |
| Basım Tarihi | 11-2025 |
| Cilt No | 13 |
| Sayı | 12 |
| Sayfalar | 1 / 14 |
| Doi Numarası | 10.3390/pr13123820 |
| Makale Linki | https://doi.org/10.3390/pr13123820 |
| Atıf Sayıları |
